Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	1738
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	107.1 KiB
Average record size in memory	63.1 B

Variable types

Numeric	8
Categorical	8

Alerts

`country` has constant value "0"	Constant
`df_index` is highly correlated with `id` and 1 other fields	High correlation
`id` is highly correlated with `df_index` and 1 other fields	High correlation
`state` is highly correlated with `df_index` and 1 other fields	High correlation
`pollutant_min` is highly correlated with `pollutant_max` and 1 other fields	High correlation
`pollutant_max` is highly correlated with `pollutant_min` and 2 other fields	High correlation
`pollutant_avg` is highly correlated with `pollutant_min` and 2 other fields	High correlation
`pollutant_id_NH3` is highly correlated with `pollutant_max` and 1 other fields	High correlation
`df_index` is highly correlated with `id` and 1 other fields	High correlation
`id` is highly correlated with `df_index` and 1 other fields	High correlation
`state` is highly correlated with `df_index` and 1 other fields	High correlation
`pollutant_min` is highly correlated with `pollutant_max` and 2 other fields	High correlation
`pollutant_max` is highly correlated with `pollutant_min` and 1 other fields	High correlation
`pollutant_avg` is highly correlated with `pollutant_min` and 1 other fields	High correlation
`pollutant_id_PM10` is highly correlated with `pollutant_min`	High correlation
`df_index` is highly correlated with `id` and 1 other fields	High correlation
`id` is highly correlated with `df_index` and 1 other fields	High correlation
`state` is highly correlated with `df_index` and 1 other fields	High correlation
`pollutant_min` is highly correlated with `pollutant_max` and 1 other fields	High correlation
`pollutant_max` is highly correlated with `pollutant_min` and 1 other fields	High correlation
`pollutant_avg` is highly correlated with `pollutant_min` and 1 other fields	High correlation
`country` is highly correlated with `pollutant_id_SO2` and 6 other fields	High correlation
`pollutant_id_SO2` is highly correlated with `country`	High correlation
`pollutant_id_NH3` is highly correlated with `country`	High correlation
`pollutant_id_NO2` is highly correlated with `country`	High correlation
`pollutant_id_PM2.5` is highly correlated with `country`	High correlation
`pollutant_id_CO` is highly correlated with `country`	High correlation
`pollutant_id_OZONE` is highly correlated with `country`	High correlation
`pollutant_id_PM10` is highly correlated with `country`	High correlation
`df_index` is highly correlated with `id` and 3 other fields	High correlation
`id` is highly correlated with `df_index` and 3 other fields	High correlation
`state` is highly correlated with `df_index` and 3 other fields	High correlation
`city` is highly correlated with `df_index` and 3 other fields	High correlation
`station` is highly correlated with `df_index` and 3 other fields	High correlation
`pollutant_min` is highly correlated with `pollutant_max` and 3 other fields	High correlation
`pollutant_max` is highly correlated with `pollutant_min` and 4 other fields	High correlation
`pollutant_avg` is highly correlated with `pollutant_min` and 3 other fields	High correlation
`pollutant_id_NH3` is highly correlated with `pollutant_max`	High correlation
`pollutant_id_PM10` is highly correlated with `pollutant_min` and 2 other fields	High correlation
`pollutant_id_PM2.5` is highly correlated with `pollutant_min` and 2 other fields	High correlation
`df_index` has unique values	Unique
`id` has unique values	Unique
`state` has 28 (1.6%) zeros	Zeros

Reproduction

Analysis started	2021-10-31 11:23:42.428467
Analysis finished	2021-10-31 11:24:03.408325
Duration	20.98 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	1738
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	906.6317606

Minimum	0
Maximum	1835
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	13.7 KiB

Quantile statistics

Minimum	0
5-th percentile	86.85
Q1	445.25
median	904.5
Q3	1366.75
95-th percentile	1728.15
Maximum	1835
Range	1835
Interquartile range (IQR)	921.5

Descriptive statistics

Standard deviation	530.7635132
Coefficient of variation (CV)	0.585423472
Kurtosis	-1.218775098
Mean	906.6317606
Median Absolute Deviation (MAD)	461
Skewness	0.01509757116
Sum	1575726
Variance	281709.9069
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.1%
1215	1	0.1%
1226	1	0.1%
1225	1	0.1%
1224	1	0.1%
1223	1	0.1%
1222	1	0.1%
1221	1	0.1%
1220	1	0.1%
1219	1	0.1%
Other values (1728)	1728	99.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%

Value	Count	Frequency (%)
1835	1	0.1%
1834	1	0.1%
1833	1	0.1%
1832	1	0.1%
1831	1	0.1%
1830	1	0.1%
1829	1	0.1%
1828	1	0.1%
1827	1	0.1%
1826	1	0.1%

id
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	1738
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	907.6317606

Minimum	1
Maximum	1836
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.7 KiB

Quantile statistics

Minimum	1
5-th percentile	87.85
Q1	446.25
median	905.5
Q3	1367.75
95-th percentile	1729.15
Maximum	1836
Range	1835
Interquartile range (IQR)	921.5

Descriptive statistics

Standard deviation	530.7635132
Coefficient of variation (CV)	0.584778471
Kurtosis	-1.218775098
Mean	907.6317606
Median Absolute Deviation (MAD)	461
Skewness	0.01509757116
Sum	1577464
Variance	281709.9069
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.1%
1216	1	0.1%
1227	1	0.1%
1226	1	0.1%
1225	1	0.1%
1224	1	0.1%
1223	1	0.1%
1222	1	0.1%
1221	1	0.1%
1220	1	0.1%
Other values (1728)	1728	99.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%
10	1	0.1%

Value	Count	Frequency (%)
1836	1	0.1%
1835	1	0.1%
1834	1	0.1%
1833	1	0.1%
1832	1	0.1%
1831	1	0.1%
1830	1	0.1%
1829	1	0.1%
1828	1	0.1%
1827	1	0.1%

country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1738

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1738	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1738	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

state
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	26
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.87802071

Minimum	0
Maximum	25
Zeros	28
Zeros (%)	1.6%
Negative	0
Negative (%)	0.0%
Memory size	6.9 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	6
median	11
Q3	21
95-th percentile	24
Maximum	25
Range	25
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	7.639101168
Coefficient of variation (CV)	0.5931890729
Kurtosis	-1.292040648
Mean	12.87802071
Median Absolute Deviation (MAD)	6
Skewness	0.3092575676
Sum	22382
Variance	58.35586666
Monotonicity	Increasing

Histogram with fixed size bins (bins=26)

Value	Count	Frequency (%)
24	287	16.5%
5	242	13.9%
7	195	11.2%
13	188	10.8%
10	183	10.5%
12	82	4.7%
6	80	4.6%
25	69	4.0%
2	66	3.8%
20	66	3.8%
Other values (16)	280	16.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	28	1.6%
1	13	0.7%
2	66	3.8%
3	14	0.8%
4	5	0.3%
5	242	13.9%
6	80	4.6%
7	195	11.2%
8	4	0.2%
9	1	0.1%

Value	Count	Frequency (%)
25	69	4.0%
24	287	16.5%
23	2	0.1%
22	35	2.0%
21	52	3.0%
20	66	3.8%
19	51	2.9%
18	7	0.4%
17	13	0.7%
16	7	0.4%

city
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	142
Distinct (%)	8.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	63.64729574

Minimum	0
Maximum	141
Zeros	2
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	6.9 KiB

Quantile statistics

Minimum	0
5-th percentile	5
Q1	33
median	58
Q3	93
95-th percentile	131.15
Maximum	141
Range	141
Interquartile range (IQR)	60

Descriptive statistics

Standard deviation	38.62069801
Coefficient of variation (CV)	0.6067924419
Kurtosis	-1.09428083
Mean	63.64729574
Median Absolute Deviation (MAD)	30.5
Skewness	0.2378790807
Sum	110619
Variance	1491.558315
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
33	242	13.9%
93	112	6.4%
17	56	3.2%
28	42	2.4%
83	41	2.4%
77	37	2.1%
107	35	2.0%
58	35	2.0%
2	35	2.0%
1	34	2.0%
Other values (132)	1069	61.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2	0.1%
1	34	2.0%
2	35	2.0%
3	3	0.2%
4	7	0.4%
5	7	0.4%
6	7	0.4%
7	7	0.4%
8	7	0.4%
9	7	0.4%

Value	Count	Frequency (%)
141	7	0.4%
140	7	0.4%
139	7	0.4%
138	7	0.4%
137	7	0.4%
136	7	0.4%
135	27	1.6%
134	5	0.3%
133	7	0.4%
132	6	0.3%

station
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	280
Distinct (%)	16.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	139.9131185

Minimum	0
Maximum	280
Zeros	7
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	6.9 KiB

Quantile statistics

Minimum	0
5-th percentile	14
Q1	70
median	140.5
Q3	210
95-th percentile	267
Maximum	280
Range	280
Interquartile range (IQR)	140

Descriptive statistics

Standard deviation	80.72199348
Coefficient of variation (CV)	0.5769437085
Kurtosis	-1.187803526
Mean	139.9131185
Median Absolute Deviation (MAD)	69.5
Skewness	0.005775083739
Sum	243169
Variance	6516.040231
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
215	7	0.4%
131	7	0.4%
19	7	0.4%
36	7	0.4%
39	7	0.4%
53	7	0.4%
109	7	0.4%
113	7	0.4%
140	7	0.4%
147	7	0.4%
Other values (270)	1668	96.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7	0.4%
1	7	0.4%
2	6	0.3%
3	7	0.4%
4	5	0.3%
5	7	0.4%
6	5	0.3%
7	6	0.3%
8	3	0.2%
9	7	0.4%

Value	Count	Frequency (%)
280	7	0.4%
279	7	0.4%
278	6	0.3%
277	6	0.3%
276	6	0.3%
275	6	0.3%
274	7	0.4%
273	6	0.3%
272	2	0.1%
271	7	0.4%

pollutant_min
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	149
Distinct (%)	8.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	28.41426928

Minimum	1
Maximum	217
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.7 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	5
median	14
Q3	39
95-th percentile	107.15
Maximum	217
Range	216
Interquartile range (IQR)	34

Descriptive statistics

Standard deviation	34.40381054
Coefficient of variation (CV)	1.210793429
Kurtosis	3.413806682
Mean	28.41426928
Median Absolute Deviation (MAD)	12
Skewness	1.851160344
Sum	49384
Variance	1183.62218
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	148	8.5%
2	116	6.7%
4	86	4.9%
3	82	4.7%
5	70	4.0%
6	67	3.9%
8	56	3.2%
10	44	2.5%
7	42	2.4%
9	35	2.0%
Other values (139)	992	57.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	148	8.5%
2	116	6.7%
3	82	4.7%
4	86	4.9%
5	70	4.0%
6	67	3.9%
7	42	2.4%
8	56	3.2%
9	35	2.0%
10	44	2.5%

Value	Count	Frequency (%)
217	1	0.1%
200	1	0.1%
193	1	0.1%
184	1	0.1%
182	1	0.1%
175	1	0.1%
172	1	0.1%
161	1	0.1%
155	2	0.1%
153	1	0.1%

pollutant_max
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	340
Distinct (%)	19.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	96.87341772

Minimum	1
Maximum	500
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.7 KiB

Quantile statistics

Minimum	1
5-th percentile	4
Q1	21
median	63
Q3	124
95-th percentile	335
Maximum	500
Range	499
Interquartile range (IQR)	103

Descriptive statistics

Standard deviation	104.7650939
Coefficient of variation (CV)	1.081463794
Kurtosis	2.488366121
Mean	96.87341772
Median Absolute Deviation (MAD)	48
Skewness	1.68591993
Sum	168366
Variance	10975.7249
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
6	47	2.7%
8	32	1.8%
3	29	1.7%
2	29	1.7%
10	28	1.6%
9	26	1.5%
7	25	1.4%
5	25	1.4%
4	25	1.4%
11	23	1.3%
Other values (330)	1449	83.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	13	0.7%
2	29	1.7%
3	29	1.7%
4	25	1.4%
5	25	1.4%
6	47	2.7%
7	25	1.4%
8	32	1.8%
9	26	1.5%
10	28	1.6%

Value	Count	Frequency (%)
500	8	0.5%
495	1	0.1%
489	1	0.1%
487	1	0.1%
477	1	0.1%
474	1	0.1%
473	1	0.1%
470	1	0.1%
469	1	0.1%
467	1	0.1%

pollutant_avg
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	237
Distinct (%)	13.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	54.10069045

Minimum	1
Maximum	314
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	13.7 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	12
median	31
Q3	70
95-th percentile	194.15
Maximum	314
Range	313
Interquartile range (IQR)	58

Descriptive statistics

Standard deviation	60.82415825
Coefficient of variation (CV)	1.124276931
Kurtosis	2.54020862
Mean	54.10069045
Median Absolute Deviation (MAD)	23
Skewness	1.72497588
Sum	94027
Variance	3699.578226
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	52	3.0%
4	50	2.9%
2	46	2.6%
6	46	2.6%
12	40	2.3%
3	39	2.2%
7	38	2.2%
10	35	2.0%
8	34	2.0%
1	30	1.7%
Other values (227)	1328	76.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	30	1.7%
2	46	2.6%
3	39	2.2%
4	50	2.9%
5	52	3.0%
6	46	2.6%
7	38	2.2%
8	34	2.0%
9	30	1.7%
10	35	2.0%

Value	Count	Frequency (%)
314	1	0.1%
309	1	0.1%
308	1	0.1%
297	1	0.1%
293	1	0.1%
290	1	0.1%
289	1	0.1%
284	1	0.1%
282	1	0.1%
280	2	0.1%

pollutant_id_CO
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1469
1	269

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1469	84.5%
1	269	15.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1469	84.5%
1	269	15.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_NH3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1520
1	218

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	1520	87.5%
1	218	12.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1520	87.5%
1	218	12.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_NO2
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1487
1	251

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1487	85.6%
1	251	14.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1487	85.6%
1	251	14.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_OZONE
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1481
1	257

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1481	85.2%
1	257	14.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1481	85.2%
1	257	14.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_PM10
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1493
1	245

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1493	85.9%
1	245	14.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1493	85.9%
1	245	14.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_PM2.5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1486
1	252

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1486	85.5%
1	252	14.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1486	85.5%
1	252	14.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

pollutant_id_SO2
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	98.6 KiB

0	1492
1	246

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	1492	85.8%
1	246	14.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1492	85.8%
1	246	14.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	id	city	station	pollutant_min	pollutant_max	pollutant_avg	pollutant_id_CO	pollutant_id_NH3	pollutant_id_NO2	pollutant_id_OZONE	pollutant_id_PM10	pollutant_id_PM2.5	pollutant_id_SO2
0	0	1	6	215	69.0	109.0	86.0	0	0	0	0	0	1	0
1	1	2	6	215	82.0	138.0	105.0	0	0	0	0	1	0	0
2	2	3	6	215	10.0	42.0	19.0	0	0	1	0	0	0	0
3	3	4	6	215	4.0	5.0	4.0	0	1	0	0	0	0	0
4	4	5	6	215	16.0	42.0	27.0	0	0	0	0	0	0	1
5	5	6	6	215	15.0	45.0	32.0	1	0	0	0	0	0	0
6	6	7	6	215	4.0	82.0	42.0	0	0	0	1	0	0	0
7	7	8	113	3	47.0	111.0	71.0	0	0	0	0	0	1	0
8	8	9	113	3	49.0	120.0	86.0	0	0	0	0	1	0	0
9	9	10	113	3	11.0	44.0	23.0	0	0	1	0	0	0	0

Last rows

	df_index	id	state	city	station	pollutant_min	pollutant_max	pollutant_avg	pollutant_id_CO	pollutant_id_NH3	pollutant_id_NO2	pollutant_id_OZONE	pollutant_id_PM10	pollutant_id_PM2.5	pollutant_id_SO2
1728	1826	1827	25	77	196	2.0	15.0	4.0	0	0	0	0	0	0	1
1729	1827	1828	25	77	196	31.0	85.0	39.0	1	0	0	0	0	0	0
1730	1828	1829	25	77	196	6.0	76.0	31.0	0	0	0	1	0	0	0
1731	1829	1830	25	77	269	28.0	75.0	54.0	0	0	0	0	0	1	0
1732	1830	1831	25	77	269	36.0	101.0	74.0	0	0	0	0	1	0	0
1733	1831	1832	25	77	269	10.0	22.0	15.0	0	0	1	0	0	0	0
1734	1832	1833	25	77	269	1.0	3.0	2.0	0	1	0	0	0	0	0
1735	1833	1834	25	77	269	6.0	28.0	10.0	0	0	0	0	0	0	1
1736	1834	1835	25	77	269	34.0	92.0	41.0	1	0	0	0	0	0	0
1737	1835	1836	25	77	269	10.0	116.0	43.0	0	0	0	1	0	0	0

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script